{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd4709c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False  True  True  True  True False  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True False  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True False  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      " False  True  True  True  True  True]\n",
      "准确率为：\n",
      " 0.96\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 42  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 49  0  0  0  0  0  2  0]\n",
      " [ 0  0  1 35  0  0  0  0  2  2]\n",
      " [ 0  0  0  0 50  0  0  1  0  0]\n",
      " [ 0  0  0  0  0 50  0  0  0  1]\n",
      " [ 0  0  0  0  0  0 44  0  1  0]\n",
      " [ 0  0  0  0  0  0  0 44  0  0]\n",
      " [ 0  2  0  1  0  1  0  0 34  1]\n",
      " [ 1  0  0  1  0  1  0  0  0 32]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99        52\n",
      "           1       0.95      1.00      0.98        42\n",
      "           2       0.98      0.96      0.97        51\n",
      "           3       0.95      0.88      0.91        40\n",
      "           4       1.00      0.98      0.99        51\n",
      "           5       0.96      0.98      0.97        51\n",
      "           6       1.00      0.98      0.99        45\n",
      "           7       0.98      1.00      0.99        44\n",
      "           8       0.87      0.87      0.87        39\n",
      "           9       0.89      0.91      0.90        35\n",
      "\n",
      "    accuracy                           0.96       450\n",
      "   macro avg       0.96      0.96      0.96       450\n",
      "weighted avg       0.96      0.96      0.96       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def forest_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=20000)\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    forest_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f850a0e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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